Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data

This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fit-and-remove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with single-structure and multiple-structure data.Comment: Accepted by European Conference on Computer Vision (ECCV

Diagnosis and Interim Treatment Outcomes from the First Cohort of Multidrug-Resistant Tuberculosis Patients in Tanzania.

Kibong'oto National Tuberculosis Hospital (KNTH), Kilimanjaro, Tanzania. Characterize the diagnostic process and interim treatment outcomes from patients treated for multidrug-resistant tuberculosis (MDR-TB) in Tanzania. A retrospective cohort study was performed among all patients treated at KNTH for pulmonary MDR-TB between November 2009 and September 2011. Sixty-one culture-positive MDR-TB patients initiated therapy, 60 (98%) with a prior history of TB treatment. Forty-one (67%) were male and 9 (14%) were HIV infected with a mean CD4 count of 424 (±106) cells/µl. The median time from specimen collection to MDR-TB diagnosis and from diagnosis to initiation of MDR-TB treatment was 138 days (IQR 101-159) and 131 days (IQR 32-233), respectively. Following treatment initiation four (7%) patients died (all HIV negative), 3 (5%) defaulted, and the remaining 54 (89%) completed the intensive phase. Most adverse drug reactions were mild to moderate and did not require discontinuation of treatment. Median time to culture conversion was 2 months (IQR 1-3) and did not vary by HIV status. In 28 isolates available for additional second-line drug susceptibility testing, fluoroquinolone, aminoglycoside and para-aminosalicylic acid resistance was rare yet ethionamide resistance was present in 9 (32%). The majority of MDR-TB patients from this cohort had survived a prolonged referral process, had multiple episodes of prior TB treatment, but did not have advanced AIDS and converted to culture negative early while completing an intensive inpatient regimen without serious adverse event. Further study is required to determine the clinical impact of second-line drug susceptibility testing and the feasibility of alternatives to prolonged hospitalization

The implications of defining obesity as a disease: a report from the Association for the Study of Obesity 2021 annual conference

Unlike various countries and organisations, including the World Health Organisation and the European Parliament, the United Kingdom does not formally recognise obesity as a disease. This report presents the discussion on the potential impact of defining obesity as a disease on the patient, the healthcare system, the economy, and the wider society. A group of speakers from a wide range of disciplines came together to debate the topic bringing their knowledge and expertise from backgrounds in medicine, psychology, economics, and politics as well as the experience of people living with obesity. The aim of their debate was not to decide whether obesity should be classified as a disease but rather to explore what the implications of doing so would be, what the gaps in the available data are, as well as to provide up-to-date information on the topic from experts in the field. There were four topics where speakers presented their viewpoints, each one including a question-and-answer section for debate. The first one focused on the impact that the recognition of obesity could have on people living with obesity regarding the change in their behaviour, either positive and empowering or more stigmatising. During the second one, the impact of defining obesity as a disease on the National Health Service and the wider economy was discussed. The primary outcome was the need for more robust data as the one available does not represent the actual cost of obesity. The third topic was related to the policy implications regarding treatment provision, focusing on the public's power to influence policy. Finally, the last issue discussed, included the implications of public health actions, highlighting the importance of the government's actions and private stakeholders. The speakers agreed that no matter where they stand on this debate, the goal is common: to provide a healthcare system that supports and protects the patients, strategies that protect the economy and broader society, and policies that reduce stigma and promote health equity. Many questions are left to be answered regarding how these goals can be achieved. However, this discussion has set a good foundation providing evidence that can be used by the public, clinicians, and policymakers to make that happen

Multi-class Model Fitting by Energy Minimization and Mode-Seeking

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used alpha-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting

Automatically Selecting Inference Algorithms for Discrete Energy Minimisation

Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed. Different inference algorithms perform better on factor graph models (GMs) from different underlying problem classes, and in general it is difficult to know which algorithm will yield the lowest energy for a given GM. To mitigate this difficulty, survey papers advise the practitioner on what algorithms perform well on what classes of models. We take the next step forward, and present a technique to automatically select the best inference algorithm for an input GM. We validate our method experimentally on an extended version of the OpenGM2 benchmark, containing a diverse set of vision problems. On average, our method selects an inference algorithm yielding labellings with 96% of variables the same as the best available algorithm

UEV-1 Is an Ubiquitin-Conjugating Enzyme Variant That Regulates Glutamate Receptor Trafficking in C. elegans Neurons

The regulation of AMPA-type glutamate receptor (AMPAR) membrane trafficking is a key mechanism by which neurons regulate synaptic strength and plasticity. AMPAR trafficking is modulated through a combination of receptor phosphorylation, ubiquitination, endocytosis, and recycling, yet the factors that mediate these processes are just beginning to be uncovered. Here we identify the ubiquitin-conjugating enzyme variant UEV-1 as a regulator of AMPAR trafficking in vivo. We identified mutations in uev-1 in a genetic screen for mutants with altered trafficking of the AMPAR subunit GLR-1 in C. elegans interneurons. Loss of uev-1 activity results in the accumulation of GLR-1 in elongated accretions in neuron cell bodies and along the ventral cord neurites. Mutants also have a corresponding behavioral defect—a decrease in spontaneous reversals in locomotion—consistent with diminished GLR-1 function. The localization of other synaptic proteins in uev-1-mutant interneurons appears normal, indicating that the GLR-1 trafficking defects are not due to gross deficiencies in synapse formation or overall protein trafficking. We provide evidence that GLR-1 accumulates at RAB-10-containing endosomes in uev-1 mutants, and that receptors arrive at these endosomes independent of clathrin-mediated endocytosis. UEV-1 homologs in other species bind to the ubiquitin-conjugating enzyme Ubc13 to create K63-linked polyubiquitin chains on substrate proteins. We find that whereas UEV-1 can interact with C. elegans UBC-13, global levels of K63-linked ubiquitination throughout nematodes appear to be unaffected in uev-1 mutants, even though UEV-1 is broadly expressed in most tissues. Nevertheless, ubc-13 mutants are similar in phenotype to uev-1 mutants, suggesting that the two proteins do work together to regulate GLR-1 trafficking. Our results suggest that UEV-1 could regulate a small subset of K63-linked ubiquitination events in nematodes, at least one of which is critical in regulating GLR-1 trafficking

Seasons and bee foraging plant species strongly influence honey antimicrobial activity

Honey has been used in human medicine since ancient times due to its antimicrobial properties. However, honey antimicrobial potential varies due to floral sources, geographical origins, and seasonality. The current study assessed the antimicrobial activity of honey and honeybees’ preferred plants namely, Acacia mellifera, Ocimum basilicum, Hoslundia opposita, Combretum schumannii, Grewia bicolor, Terminalia brownii, Cordia monoica from Same district in Northern Tanzania, during the short and long rain seasons of 2021/2022. The agar well diffusion method was employed for the antimicrobial assay, and the antimicrobial activity was evaluated by measuring inhibition zones. Significant differences were observed in antimicrobial activities among honey of different seasons (F = 28.5, p = <0.001) and plant extracts (F = 15.9, p < 0.001). Honey A and D that were harvested at the end of the short rain season were found with higher antimicrobial activities (10–19 mm inhibition) than that harvested at the end of the long rain season (10–15 mm inhibition), and the most susceptible microorganisms were Escherichia coli and Staphylococcus aureus. For the tested plant extracts, T. brownii, C. schumannii, and H. opposita showed higher antimicrobial activities (11.3–19 mm inhibition) against pathogenic microorganisms than other tested plants. There was a strong positive correlation in antimicrobial activities (r = 0.836, p = 0.078, r = 0.756, p = 0.139, and r = 0.732 p = 0.159) between honey harvested at the end of the short rain season with some plant extracts from plants blooming during the same season. The study highlighted the variation in antimicrobial activities among honey harvested in different rain seasons and that there is antimicrobial relation between honey and plants that are foraged by honeybees. Thus, the antimicrobial ability of the honey depends much on the plant species foraged by honeybees